Background Numerous studies have found that areas with higher alcohol establishment density are more likely to have higher violent crime rates but many of these studies did not assess the differential effects of type of establishments or the effects on multiple categories of crime. In this study, we assess whether alcohol establishment density is associated with four categories of violent crime, and whether the strength of the associations varies by type of violent crime and by on-premise establishments (e.g., bars, restaurants) versus off-premise establishments (e.g., liquor and convenience stores). Methods Data come from the city of Minneapolis, Minnesota in 2009 and were aggregated and analyzed at the neighborhood level. Across the 83 neighborhoods in Minneapolis, we examined four categories of violent crime: assault, rape, robbery, and total violent crime. We used a Bayesian hierarchical inference approach to model the data, accounting for spatial auto-correlation and controlling for relevant neighborhood demographics. Models were estimated for total alcohol establishment density as well as separately for on-premise establishments and off-premise establishments. Results Positive, statistically significant associations were observed for total alcohol establishment density and each of the violent crime outcomes. We estimate that a 3.9% to 4.3% increase across crime categories would result from a 20% increase in neighborhood establishment density. The associations between on-premise density and each of the individual violent crime outcomes were also all positive and significant and similar in strength as for total establishment density. The relationships between off-premise density and the crime outcomes were all positive but not significant for rape or total violent crime, and the strength of the associations was weaker than those for total and on-premise density. Conclusions Results of this study, combined with earlier findings, provide more evidence that community leaders should be cautious about increasing the density of alcohol establishments within their neighborhoods.

Background Untreated HIV infection is associated with changes in blood lipids, inflammation, thrombotic activity, and increased risk for CVD. Methods We studied high-density lipoprotein particle (HDLp) concentrations and inflammatory (hsCRP, IL-6), endothelial activation (E-selectin, sICAM-1) and thrombotic (fibrinogen and D-dimer) biomarkers in 32 untreated HIV-infected and 29 uninfected persons. Differences in blood lipids and biomarkers by HIV status were examined before and after adjustment for: age, gender, race/ethnicity, smoking status, BMI, and hepatitis C. Results HIV-infected, versus uninfected, participants had lower HDLc (−26%) and total (−21%), large (−50%)and small HDLp (−20%; p≤0.01 for all), but not medium HDLp. A trend was present for higher total cholesterol (p=0.15) and triglycerides (p=0.11) with HIV infection. Levels of IL-6, sICAM-1 and D-dimer were 65–70% higher in HIV-infected participants (p≤0.02 for all). Covariate adjustment did not diminish these associations. For HIV-infected participants, total and small HDLp (respectively) tended to correlate inversely with levels of IL-6 (p=0.08 and p=0.02), sICAM-1 (p<0.01 for both) and D-dimer (p=0.03 and p<0.01). Conclusions Persons with untreated HIV infection have lower HDLp, primarily large and small HDLp, and higher IL-6, sICAM-1, and D-dimer levels, and the relationship of these markers with risk for HIV-mediated atherosclerotic risk requires further study.

Background Although many studies have documented the dramatic declines in heart disease mortality in the United States at the national level, little attention has been given to the temporal changes in the geographic patterns of heart disease mortality. Methods and Results Age-adjusted and spatially smoothed county-level heart disease death rates were calculated for 2-year intervals from 1973 to 1974 to 2009 to 2010 for those aged ≥35 years. Heart disease deaths were defined according to the International Classification of Diseases codes for diseases of the heart in the eighth, ninth, and tenth revisions of the International Classification of Diseases. A fully Bayesian spatiotemporal model was used to produce precise rate estimates, even in counties with small populations. A substantial shift in the concentration of high-rate counties from the Northeast to the Deep South was observed, along with a concentration of slow-decline counties in the South and a nearly 2-fold increase in the geographic inequality among counties. Conclusions The dramatic change in the geographic patterns of heart disease mortality during 40 years highlights the importance of small-area surveillance to reveal patterns that are hidden at the national level, gives communities the historical context for understanding their current burden of heart disease, and provides important clues for understanding the determinants of the geographic disparities in heart disease mortality.

The concept of a so-called urban advantage in health ignores the possibility of heterogeneity in health outcomes across cities. Using a harmonized dataset from the SALURBAL project, we describe variability and predictors of life expectancy and proportionate mortality in 363 cities across nine Latin American countries. Life expectancy differed substantially across cities within the same country. Cause-specific mortality also varied across cities, with some causes of death (unintentional and violent injuries and deaths) showing large variation within countries, whereas other causes of death (communicable, maternal, neonatal and nutritional, cancer, cardiovascular disease and other noncommunicable diseases) varied substantially between countries. In multivariable mixed models, higher levels of education, water access and sanitation and less overcrowding were associated with longer life expectancy, a relatively lower proportion of communicable, maternal, neonatal and nutritional deaths and a higher proportion of deaths from cancer, cardiovascular disease and other noncommunicable diseases. These results highlight considerable heterogeneity in life expectancy and causes of death across cities of Latin America, revealing modifiable factors that could be amenable to urban policies aimed toward improving urban health in Latin America and more generally in other urban environments.

BackgroundExamining small‐area differences in the strength of declining heart disease mortality by race and sex provides important context for current racial and geographic disparities and identifies localities that could benefit from targeted interventions. We identified and described temporal trends in declining county‐level heart disease mortality by race, sex, and geography between 1973 and 2010.Methods and ResultsUsing a Bayesian hierarchical model, we estimated age‐adjusted mortality with diseases of the heart listed as the underlying cause for 3099 counties. County‐level percentage declines were calculated by race and sex for 3 time periods (1973–1985, 1986–1997, 1998–2010). Strong declines were statistically faster or no different than the total national decline in that time period. We observed county‐level race–sex disparities in heart disease mortality trends. Continual (from 1973 to 2010) strong declines occurred in 73.2%, 44.6%, 15.5%, and 17.3% of counties for white men, white women, black men, and black women, respectively. Delayed (1998–2010) strong declines occurred in 15.4%, 42.0%, 75.5%, and 76.6% of counties for white men, white women, black men, and black women, respectively. Counties with the weakest patterns of decline were concentrated in the South.ConclusionsSince 1973, heart disease mortality has declined substantially for these race–sex groups. Patterns of decline differed by race and geography, reflecting potential disparities in national and local drivers of these declines. Better understanding of racial and geographic disparities in the diffusion of heart disease prevention and treatment may allow us to find clues to progress toward racial and geographic equity in heart disease mortality.

Background Untreated HIV infection may increase risk for cardiovascular disease, and arterial elasticity is a marker of cardiovascular risk and early disease. Methods HIV-infected participants not taking antiretroviral therapy (n = 32) were compared with HIV-negative controls (n = 30). Large and small artery elasticity (LAE and SAE) were estimated via analysis of radial pulse waveforms. Differences in LAE and SAE by HIV status were compared using analysis of covariance, with and without adjustment for Framingham risk (model 1); covariates that differed between groups [smoking, injection drug use, hepatitis C, and high-density lipoprotein cholesterol (HDLc); model 2]; or age, sex, race/ethnicity, smoking, injection drug use, hepatitis C, HDLc, and non-HDLc (model 3). Results HIV infection was associated with impaired LAE (−2.55 mL/mm Hg × 10; P = 0.02) and SAE (−1.50 mL/mm Hg × 100; P = 0.02). Associations with traditional risk factors were often stronger for SAE than LAE, including with Framingham score (per 1% higher; SAE −0.18, P = 0.01; LAE −0.19, P = 0.13). Fasting lipid levels were not significantly associated with LAE and SAE. After adjustment, differences between HIV-infected and HIV-uninfected participants were similar in model 1 (−2.36 for LAE, P = 0.04; −1.31 for SAE, P = 0.04), model 2 (−2.67 for LAE, P = 0.02; −1.13 for SAE, P = 0.07) and model 3 (−2.91 for LAE, P = 0.02; −1.34 for SAE, P = 0.03). CD4 count and HIV RNA level were not associated with LAE and SAE among HIV-infected participants. Conclusions Untreated HIV infection is associated with impaired arterial elasticity, of both the large and small vasculature, after controlling for additional risk factors. Pulse waveform analysis is a noninvasive technique to assess cardiovascular disease risk that should be evaluated in larger studies of HIV-infected persons.

Many data stewards collect confidential data that include fine geography. When sharing these data with others, data stewards strive to disseminate data that are informative for a wide range of spatial and non-spatial analyses while simultaneously protecting the confidentiality of data subjects' identities and attributes. Typically, data stewards meet this challenge by coarsening the resolution of the released geography and, as needed, perturbing the confidential attributes. When done with high intensity, these redaction strategies can result in released data with poor analytic quality. We propose an alternative dissemination approach based on fully synthetic data. We generate data using marked point process models that can maintain both the statistical properties and the spatial dependence structure of the confidential data. We illustrate the approach using data consisting of mortality records from Durham, North Carolina.

Advances in Geographical Information Systems (GIS) have led to the enormous recent burgeoning of spatial-temporal databases and associated statistical modeling. Here we depart from the rather rich literature in space–time modeling by considering the setting where space is discrete (e.g., aggregated data over regions), but time is continuous. Our major objective in this application is to carry out inference on gradients of a temporal process in our data set of monthly county level asthma hospitalization rates in the state of California, while at the same time accounting for spatial similarities of the temporal process across neighboring counties. Use of continuous time models here allows inference at a finer resolution than at which the data are sampled. Rather than use parametric forms to model time, we opt for a more flexible stochastic process embedded within a dynamic Markov random field framework. Through the matrix-valued covariance function we can ensure that the temporal process realizations are mean square differentiable, and may thus carry out inference on temporal gradients in a posterior predictive fashion. We use this approach to evaluate temporal gradients where we are concerned with temporal changes in the residual and fitted rate curves after accounting for seasonality, spatiotemporal ozone levels and several spatially-resolved important sociodemographic covariates.